EVOLVING COEVOLUTIONARY CLASSIFIERS UNDER LARGE ATTRIBUTE SPACES

Model-building under the supervised learning domain potentially face a dual learning problem of identifying both the parameters of the model and the subset of (domain) attributes necessary to support the model, thus using an embedded as opposed to wrapper or filter based design. Genetic Programming (GP) has always addressed this dual problem, however, further implicit assumptions are made which potentially increase the complexity of the resulting solutions. In this work we are specifically interested in the case of classification under very large attribute spaces. As such it might be expected that multiple independent/overlapping attribute subspaces support the mapping to class labels; whereas GP approaches to classification generally assume a single binary classifier per class, forcing the model to provide a solution in terms of a single attribute subspace and single mapping to class labels. Supporting the more general goal is considered as a requirement for identifying a 'team' of classifiers with non-overlapping classifier behaviors, in which each classifier responds to different subsets of exemplars. Moreover, the subsets of attributes associated with each team member might utilize a unique 'subspace' of attributes. This work investigates the utility of coevolutionary model building for the case of classification problems with attribute vectors consisting of 650 to 100,000 dimensions. The resulting team based coevolutionary evolutionary method-Symbiotic Bid-based (SBB) GP-is compared to alternative embedded classifier approaches of C4.5 and Maximum Entropy Classification (MaxEnt). SSB solutions demonstrate up to an order of magnitude lower attribute count relative to C4.5 and up to two orders of magnitude lower attribute count than MaxEnt while retaining comparable or better classification performance. Moreover, relative to the attribute count of individual models participating within a team, no more than six attributes are ever utilized; adding a further level of simplicity to the resulting solutions.